Collaborative Modeling to Compare Different Breast Cancer Screening Strategies: A Decision Analysis for the US Preventive Services Task Force

Importance

The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known.

Objective

To estimate outcomes of various mammography screening strategies.

Design, Setting, and Population

Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses.

Exposures

Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and "real-world" treatment.

Main Outcomes and Measures

Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, overdiagnosis), and number of mammograms per 1000 women.

Results

Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening) was associated with a 30.0% breast cancer mortality reduction, 1376 false-positive recalls, and 14 overdiagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and overdiagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women.

Conclusions

This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening for women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities.

Benefits and Harms of Mammography Screening in 75 + Women to Inform Shared Decision-making: a Simulation Modeling Study

BACKGROUND

Guidelines recommend shared decision-making (SDM) around mammography screening for women ≥ 75 years old.

OBJECTIVE

To use microsimulation modeling to estimate the lifetime benefits and harms of screening women aged 75, 80, and 85 years based on their individual risk factors (family history, breast density, prior biopsy) and comorbidity level to support SDM in clinical practice.

DESIGN, SETTING, AND PARTICIPANTS

We adapted two established Cancer Intervention and Surveillance Modeling Network (CISNET) models to evaluate the remaining lifetime benefits and harms of screening U.S. women born in 1940, at decision ages 75, 80, and 85 years considering their individual risk factors and comorbidity levels. Results were summarized for average- and higher-risk women (defined as having breast cancer family history, heterogeneously dense breasts, and no prior biopsy, 5% of the population).

MAIN OUTCOMES AND MEASURES

Remaining lifetime breast cancers detected, deaths (breast cancer/other causes), false positives, and overdiagnoses for average- and higher-risk women by age and comorbidity level for screening (one or five screens) vs. no screening per 1000 women.

RESULTS

Compared to stopping, one additional screen at 75 years old resulted in six and eight more breast cancers detected (10% overdiagnoses), one and two fewer breast cancer deaths, and 52 and 59 false positives per 1000 average- and higher-risk women without comorbidities, respectively. Five additional screens over 10 years led to 23 and 31 additional breast cancer cases (29-31% overdiagnoses), four and 15 breast cancer deaths avoided, and 238 and 268 false positives per 1000 average- and higher-risk screened women without comorbidities, respectively. Screening women at older ages (80 and 85 years old) and high comorbidity levels led to fewer breast cancer deaths and a higher percentage of overdiagnoses.

CONCLUSIONS

Simulation models show that continuing screening in women ≥ 75 years old results in fewer breast cancer deaths but more false positive tests and overdiagnoses. Together, clinicians and 75 + women may use model output to weigh the benefits and harms of continued screening.

Analysis of Breast Cancer Mortality in the US-1975 to 2019

Importance

Breast cancer mortality in the US declined between 1975 and 2019. The association of changes in metastatic breast cancer treatment with improved breast cancer mortality is unclear.

Objective

To simulate the relative associations of breast cancer screening, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer with improved breast cancer mortality.

Design, Setting, and Participants

Using aggregated observational and clinical trial data on the dissemination and effects of screening and treatment, 4 Cancer Intervention and Surveillance Modeling Network (CISNET) models simulated US breast cancer mortality rates. Death due to breast cancer, overall and by estrogen receptor and ERBB2 (formerly HER2) status, among women aged 30 to 79 years in the US from 1975 to 2019 was simulated.

Exposures

Screening mammography, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer.

Main Outcomes and Measures

Model-estimated age-adjusted breast cancer mortality rate associated with screening, stage I to III treatment, and metastatic treatment relative to the absence of these exposures was assessed, as was model-estimated median survival after breast cancer metastatic recurrence.

Results

The breast cancer mortality rate in the US (age adjusted) was 48/100 000 women in 1975 and 27/100 000 women in 2019. In 2019, the combination of screening, stage I to III treatment, and metastatic treatment was associated with a 58% reduction (model range, 55%-61%) in breast cancer mortality. Of this reduction, 29% (model range, 19%-33%) was associated with treatment of metastatic breast cancer, 47% (model range, 35%-60%) with treatment of stage I to III breast cancer, and 25% (model range, 21%-33%) with mammography screening. Based on simulations, the greatest change in survival after metastatic recurrence occurred between 2000 and 2019, from 1.9 years (model range, 1.0-2.7 years) to 3.2 years (model range, 2.0-4.9 years). Median survival for estrogen receptor (ER)-positive/ERBB2-positive breast cancer improved by 2.5 years (model range, 2.0-3.4 years), whereas median survival for ER-/ERBB2- breast cancer improved by 0.5 years (model range, 0.3-0.8 years).

Conclusions and Relevance

According to 4 simulation models, breast cancer screening and treatment in 2019 were associated with a 58% reduction in US breast cancer mortality compared with interventions in 1975. Simulations suggested that treatment for stage I to III breast cancer was associated with approximately 47% of the mortality reduction, whereas treatment for metastatic breast cancer was associated with 29% of the reduction and screening with 25% of the reduction.

A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States

Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%-96%), insured (99%; 97%-100%) women, with college education or higher (60%; 27%-100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9-39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools.

Highlights

There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening.Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes.Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.

Opportunities, challenges, and future directions for simulation modeling the effects of structural racism on cancer mortality in the United States: a scoping review

PURPOSE

Structural racism could contribute to racial and ethnic disparities in cancer mortality via its broad effects on housing, economic opportunities, and health care. However, there has been limited focus on incorporating structural racism into simulation models designed to identify practice and policy strategies to support health equity. We reviewed studies evaluating structural racism and cancer mortality disparities to highlight opportunities, challenges, and future directions to capture this broad concept in simulation modeling research.

METHODS

We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Articles published between 2018 and 2023 were searched including terms related to race, ethnicity, cancer-specific and all-cause mortality, and structural racism. We included studies evaluating the effects of structural racism on racial and ethnic disparities in cancer mortality in the United States.

RESULTS

A total of 8345 articles were identified, and 183 articles were included. Studies used different measures, data sources, and methods. For example, in 20 studies, racial residential segregation, one component of structural racism, was measured by indices of dissimilarity, concentration at the extremes, redlining, or isolation. Data sources included cancer registries, claims, or institutional data linked to area-level metrics from the US census or historical mortgage data. Segregation was associated with worse survival. Nine studies were location specific, and the segregation measures were developed for Black, Hispanic, and White residents.

CONCLUSIONS

A range of measures and data sources are available to capture the effects of structural racism. We provide a set of recommendations for best practices for modelers to consider when incorporating the effects of structural racism into simulation models.

Population simulation modeling of disparities in US breast cancer mortality

BACKGROUND

Populations of African American or Black women have persistently higher breast cancer mortality than the overall US population, despite having slightly lower age-adjusted incidence.

METHODS

Three Cancer Intervention and Surveillance Modeling Network simulation teams modeled cancer mortality disparities between Black female populations and the overall US population. Model inputs used racial group-specific data from clinical trials, national registries, nationally representative surveys, and observational studies. Analyses began with cancer mortality in the overall population and sequentially replaced parameters for Black populations to quantify the percentage of modeled breast cancer morality disparities attributable to differences in demographics, incidence, access to screening and treatment, and variation in tumor biology and response to therapy.

RESULTS

Results were similar across the 3 models. In 2019, racial differences in incidence and competing mortality accounted for a net ‒1% of mortality disparities, while tumor subtype and stage distributions accounted for a mean of 20% (range across models = 13%-24%), and screening accounted for a mean of 3% (range = 3%-4%) of the modeled mortality disparities. Treatment parameters accounted for the majority of modeled mortality disparities: mean = 17% (range = 16%-19%) for treatment initiation and mean = 61% (range = 57%-63%) for real-world effectiveness.

CONCLUSION

Our model results suggest that changes in policies that target improvements in treatment access could increase breast cancer equity. The findings also highlight that efforts must extend beyond policies targeting equity in treatment initiation to include high-quality treatment completion. This research will facilitate future modeling to test the effects of different specific policy changes on mortality disparities.

A health equity framework to support the next generation of cancer population simulation models

Over the past 2 decades, population simulation modeling has evolved as an effective public health tool for surveillance of cancer trends and estimation of the impact of screening and treatment strategies on incidence and mortality, including documentation of persistent cancer inequities. The goal of this research was to provide a framework to support the next generation of cancer population simulation models to identify leverage points in the cancer control continuum to accelerate achievement of equity in cancer care for minoritized populations. In our framework, systemic racism is conceptualized as the root cause of inequity and an upstream influence acting on subsequent downstream events, which ultimately exert physiological effects on cancer incidence and mortality and competing comorbidities. To date, most simulation models investigating racial inequity have used individual-level race variables. Individual-level race is a proxy for exposure to systemic racism, not a biological construct. However, single-level race variables are suboptimal proxies for the multilevel systems, policies, and practices that perpetuate inequity. We recommend that future models designed to capture relationships between systemic racism and cancer outcomes replace or extend single-level race variables with multilevel measures that capture structural, interpersonal, and internalized racism. Models should investigate actionable levers, such as changes in health care, education, and economic structures and policies to increase equity and reductions in health-care-based interpersonal racism. This integrated approach could support novel research approaches, make explicit the effects of different structures and policies, highlight data gaps in interactions between model components mirroring how factors act in the real world, inform how we collect data to model cancer equity, and generate results that could inform policy.

Reassessing the Benefits and Harms of Risk-Reducing Medication Considering the Persistent Risk of Breast Cancer Mortality in Estrogen Receptor-Positive Breast Cancer

PURPOSE

Recent studies, including a meta-analysis of 88 trials, have shown higher than expected rates of recurrence and death in hormone receptor-positive breast cancer. These new findings suggest a need to re-evaluate the use of risk-reducing medication to avoid invasive breast cancer and breast cancer death in high-risk women.

METHODS

We adapted an established Cancer Intervention and Surveillance Modeling Network model to evaluate the lifetime benefits and harms of risk-reducing medication in women with a ≥ 3% 5-year risk of developing breast cancer according to the Breast Cancer Surveillance Consortium risk calculator. Model input parameters were derived from meta-analyses, clinical trials, and large observational data. We evaluated the effects of 5 years of risk-reducing medication (tamoxifen/aromatase inhibitors) with annual screening mammography ± magnetic resonance imaging (MRI) compared with no screening, MRI, or risk-reducing medication. The modeled outcomes included invasive breast cancer, breast cancer death, side effects, false positives, and overdiagnosis. We conducted subgroup analyses for individual risk factors such as age, family history, and prior biopsy.

RESULTS

Risk-reducing tamoxifen with annual screening (± MRI) decreased the risk of invasive breast cancer by 40% and breast cancer death by 57%, compared with no tamoxifen or screening. This is equivalent to an absolute reduction of 95 invasive breast cancers, and 42 breast cancer deaths per 1,000 high-risk women. However, these drugs are associated with side effects. For example, tamoxifen could increase the number of endometrial cancers up to 11 per 1,000 high-risk women. Benefits and harms varied by individual characteristics.

CONCLUSION

The addition of risk-reducing medication to screening could further decrease the risk of breast cancer death. Clinical guidelines for high-risk women should consider integrating shared decision making for risk-reducing medication and screening on the basis of individual risk factors.

Using Protection Motivation Theory to Predict Intentions for Breast Cancer Risk Management: Intervention Mechanisms from a Randomized Controlled Trial

The purpose of this study is to evaluate the direct and indirect effects of a web-based, Protection Motivation Theory (PMT)-informed breast cancer education and decision support tool on intentions for risk-reducing medication and breast MRI among high-risk women. Women with ≥ 1.67% 5-year breast cancer risk (N = 995) were randomized to (1) control or (2) the PMT-informed intervention. Six weeks post-intervention, 924 (93% retention) self-reported PMT constructs and behavioral intentions. Bootstrapped mediations evaluated the direct effect of the intervention on behavioral intentions and the mediating role of PMT constructs. There was no direct intervention effect on intentions for risk-reducing medication or MRI (p's ≥ 0.12). There were significant indirect effects on risk-reducing medication intentions via perceived risk, self-efficacy, and response efficacy, and on MRI intentions via perceived risk and response efficacy (p's ≤ 0.04). The PMT-informed intervention effected behavioral intentions via perceived breast cancer risk, self-efficacy, and response efficacy. Future research should extend these findings from intentions to behavior. ClinicalTrials.gov Identifier: NCT03029286 (date of registration: January 24, 2017).

A Randomized Trial of Telephone-Based Smoking Cessation Treatment in the Lung Cancer Screening Setting

BACKGROUND

Lung cancer mortality is reduced via low-dose CT screening and treatment of early-stage disease. Evidence-based smoking cessation treatment in the lung screening setting can further reduce mortality. We report the results of a cessation trial from the NCI's SCALE collaboration.

METHODS

Eligible patients (N = 818) aged 50-80 were randomized (May 2017-January 2021) to the Intensive vs. Minimal arms (8 vs. 3 phone sessions plus 8 vs. 2 weeks of nicotine patches, respectively). Bio-verified (primary) and self-reported 7-day abstinence rates were assessed 3-, 6-, and 12-months post-randomization. Logistic regression analyses evaluated the effects of study arm. All statistical tests were two-sided.

RESULTS

Participants reported 48.0 (SD = 17.2) pack-years and 51.6% were not ready to quit in < 30 days. Self-reported 3-month quit rates were significantly higher in the Intensive vs. Minimal arm (14.3% vs. 7.9%; OR = 2.00, 95% confidence interval [CI] = 1.26,3.18). Bio-verified abstinence was lower but with similar relative differences between arms (9.1% vs. 3.9%; OR = 2.70, 95% CI = 1.44, 5.08). Compared to the Minimal arm, the Intensive arm was more effective among those with greater nicotine dependence (OR = 3.47, 95% CI = 1.55, 7.76), normal screening results (OR = 2.58, 95% CI = 1.32, 5.03), high engagement in counseling (OR = 3.03, 95% CI = 1.50, 6.14) and patch use (OR = 2.81, 95% CI = 1.39, 5.68). Abstinence rates did not differ significantly between arms at 6-months (OR = 1.2, 95% CI = 0.68, 2.11) or 12-months (OR = 1.4, 95% CI = 0.82, 2.42).

CONCLUSIONS

Delivering intensive telephone counseling and nicotine replacement with lung screening is an effective strategy to increase short-term smoking cessation. Methods to maintain short-term effects are needed. Even with modest quit rates, integrating cessation treatment into lung screening programs may have a large impact on tobacco-related mortality.

Cost-Effectiveness of a Telephone-Based Smoking Cessation Randomized Trial in the Lung Cancer Screening Setting

BACKGROUND

There are limited data on the cost-effectiveness of smoking cessation interventions in lung cancer screening settings. We conducted an economic analysis embedded in a national randomized trial of 2 telephone counseling cessation interventions.

METHODS

We used a societal perspective to compare the short-term cost per 6-month bio-verified quit and long-term cost-effectiveness of the interventions. Trial data were used to micro-cost intervention delivery, and the data were extended to a lifetime horizon using an established Cancer Intervention Surveillance and Modeling Network lung cancer model. We modeled the impact of screening accompanied by 8 weeks vs 3 weeks of telephone counseling (plus nicotine replacement) vs screening alone based on 2021 screening eligibility. Lifetime downstream costs (2021 dollars) and effects (life-years gained, quality-adjusted life-years [QALYs]) saved were discounted at 3%. Sensitivity analyses tested the effects of varying quit rates and costs; all analyses assumed nonrelapse after quitting.

RESULTS

The costs for delivery of the 8-week vs 3-week protocol were $380.23 vs $144.93 per person, and quit rates were 7.14% vs 5.96%, respectively. The least costly strategy was a 3-week counseling approach. An 8-week (vs 3-week) counseling approach increased costs but gained QALYs for an incremental cost-effectiveness ratio of $4029 per QALY. Screening alone cost more and saved fewer QALYs than either counseling strategy. Conclusions were robust in sensitivity analyses.

CONCLUSIONS

Telephone-based cessation interventions with nicotine replacement are considered cost-effective in the lung screening setting. Integrating smoking cessation interventions with lung screening programs has the potential to maximize long-term health benefits at reasonable costs.

Abstract 5941: A simulation modeling study to support personalized breast cancer prevention and early detection in high-risk women

Purpose: To evaluate the impact of early detection with screening and primary prevention with risk-reducing medication to provide personalized data that will help identify women who are more likely to benefit from various interventions or combinations of interventions with the least harms.

Methods: We adapted the CISNET microsimulation model G-E of breast cancer natural history to evaluate the harms and benefits of annual mammography and risk reducing medication among high-risk women (i.e., 5-year risk greater than or equal to 3%). Model G-E is a discrete event microsimulation model that follows millions of women from birth to death and captures the variability in distributions of each event. Each simulated woman is assigned a cohort-specific life expectancy which is used to select a date of breast cancer death. For this study, we dynamically updated the risk of developing breast cancer for each simulated woman based on her family history, breast density, age and history of biopsy. We used large observational and clinical trial data to derive input parameters for cohort-specific birth rates, incidence and stage without screening, other cause mortality by age, screening performance (sensitivity/specificity), survival by age, stage, and subtype without treatment, treatment efficacy, and other cause mortality. We compared model outcomes for screening alone vs. screening with a 5-year course of risk reducing medication. We modeled various screening strategies including annual or biennial screening starting at ages 35, 40, 45 and stopping at ages 65 and 74 years. Model outcomes for each strategy included, the benefits of risk-reducing drugs (avoiding breast cancer) and screening (breast cancer stage, breast cancer-specific survival), and harms of screening (false positives, overdiagnosis). We also conducted sensitivity analysis to estimate the effects of uncertainty in model inputs or assumptions on results.

Results: We found that risk reducing medication could result in an additional 28% decrease in invasive breast cancer incidence, 20% decrease in stage IV diagnosis, and a 30% decrease in breast cancer death if screening started at age 35 in a high-risk woman. However, potential breast density changes due to risk reducing medication among high-risk women could result in a 19% increase in false positive screening results compared to screening alone. The results varied by the starting age of screening.

Conclusions: Simulation modeling is useful in assessing the relative benefits and harms of screening and risk reducing medication in high-risk women.

Citation Format: Jinani Jayasekera, Amy Zhao. A simulation modeling study to support personalized breast cancer prevention and early detection in high-risk women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5941.

Simulation modeling as a tool to support clinical guidelines and care for breast cancer prevention and early detection in high-risk women

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Background: To evaluate the incremental short- and long-term benefits and harms of primary prevention with risk reducing medication in high-risk women receiving screening mammography. Methods: We adapted an established, validated Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer discrete event microsimulation model developed to synthesize data the impact of using risk-reducing medication and annual mammography among women with a 3% or higher five-year risk of developing breast cancer. We also examined the effects of supplemental MRI. The model follows a simulated cohort of millions of US women from birth to death. We used large observational and clinical trial data to derive input parameters for cohort-specific birth rates, breast cancer risk, incidence and stage, screening performance, survival by age, stage, and subtype, treatment efficacy, and other cause mortality. Breast cancer risk was modeled based on family history, breast density, age and history of past breast biopsy. We compared two strategies, annual 3D mammography alone vs. annual 3D mammography and a 5-year course of risk reducing medication at various starting ages, and adding MRI to each approach. Outcomes included the benefits of risk-reducing drugs (avoiding breast cancer) and screening (stage, breast cancer death). Harms included drug side effects and screening false positives and overdiagnosis. Sensitivity analysis tested the impact of uncertainty in model inputs and assumptions on results. Results: Compared to mammography alone, adding risk reducing medication could decrease invasive breast cancer incidence by 30%, and breast cancer deaths by 30% (Table). However, due to reduction in breast cancer incidence, risk reducing medication could result in a 3% increase in false positive results; adding MRI increases benefits but also increases false-positive results. Benefits and harms of risk reducing medication and breast cancer screening strategies for women at high-risk of developing breast cancer. Conclusions: Risk-reducing mediation reduces the risk of hormone-receptor positive breast cancer, and combining this with mammography (and/or MRI) improves earlier detection. Additional work is ongoing to incorporate adverse effects of therapy. Simulation modeling can be used to provide individualized data to facilitate discussions about breast cancer prevention and early detection among high-risk women seen in clinical practice.[Table: see text]

Contributions of screening, early-stage treatment, and metastatic treatment to breast cancer mortality reduction by molecular subtype in U.S. women, 2000-2017

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Background: Treatment for metastatic breast cancer has advanced since 2000, but we do not know if those advances have reduced mortality in the general population. Methods: Four Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2017 using national data on mammography use and performance, efficacy and dissemination of estrogen receptor (ER) and HER2-specific treatments of early-stage (stages I-III) and metastatic (stage IV or distant recurrence) disease, and competing mortality. Models compared overall and ER/HER2-specific breast cancer mortality rates from 2000 to 2017 relative to estimated rates with no screening or treatment, and attributed mortality reductions to screening, early-stage or metastatic treatment. Results of an exemplar model are shown. Results: The mortality reduction attributable to early-stage treatment increased from 35.8% in 2000 to 48.2% in 2017, while the proportion attributable to metastatic treatment decreased slightly from 23.9% to 20.6%. The increasing contribution of early-stage treatment reflects the transition of effective metastatic treatments to early-stage disease: accordingly, ten-year distant recurrence-free survival improved (82.5% in 2000, 87.3% in 2017; for ER+HER2+, 78.2% to 90.9%). Survival time after metastatic diagnosis also increased, doubling from 1.48 years in 2000 to 2.80 years in 2017, with the best survival for women with ER+HER2+ cancers (4.08 years) and worst for ER-HER2- (1.22 years). Conclusions: Advances in metastatic breast cancer treatment are reflected in lower population mortality, both through transition to early-stage treatment and gains for women with metastatic disease. These results may inform patient/physician discussions about breast cancer prognosis and expected benefits of treatment. [Table: see text]

A scoping review of interactive and personalized web-based clinical tools to support treatment decision making in breast cancer

The increasing attention on personalized breast cancer care has resulted in an explosion of new interactive, tailored, web-based clinical decision tools for guiding treatment decisions in clinical practice. The goal of this study was to review, compare, and discuss the clinical implications of current tools, and highlight future directions for tools aiming to improve personalized breast cancer care. We searched PubMed, Embase, PsychInfo, Cochrane Database of Systematic Reviews, Web of Science, and Scopus to identify web-based decision tools addressing breast cancer treatment decisions. There was a total of 17 articles associated with 21 unique tools supporting decisions related to surgery, radiation therapy, hormonal therapy, bisphosphonates, HER2-targeted therapy, and chemotherapy. The quality of the tools was assessed using the International Patient Decision Aid Standard instrument. Overall, the tools considered clinical (e.g., age) and tumor characteristics (e.g., grade) to provide personalized outcomes (e.g., survival) associated with various treatment options. Fewer tools provided the adverse effects of the selected treatment. Only one tool was field-tested with patients, and none were tested with healthcare providers. Future studies need to assess the feasibility, usability, acceptability, as well as the effects of personalized web-based decision tools on communication and decision making from the patient and clinician perspectives.

Simulation modeling of breast cancer endocrine therapy duration by patient and tumor characteristics

Background

Extending endocrine therapy from 5 to 10 years is recommended for women with invasive estrogen receptor (ER)‐positive breast cancers. We evaluated the benefits and harms of the five additional years of therapy.

Methods

An established Cancer Intervention and Surveillance Network (CISNET) model used a lifetime horizon with national and clinical trial data on treatment efficacy and adverse events and other‐cause mortality among multiple birth cohorts of U.S. women ages 25–79 newly diagnosed with ER+, non‐metastatic breast cancer. We assumed 100% use of therapy. Outcomes included life years (LYs), quality‐adjusted life years (QALYs), and breast cancer mortality. Results were discounted at 3%. Sensitivity analyses tested a 15‐year time horizon and alternative assumptions.

Results

Extending tamoxifen therapy duration among women ages 25–49 reduced the lifetime probability of breast cancer death from 11.9% to 9.3% (absolute difference 2.6%). This translates to a gain of 0.77 LYs (281 days)/woman (undiscounted). Adverse events reduce this gain to 0.44 QALYs and after discounting, gains are 0.20 QALYs (73 days)/woman. Extended aromatase inhibitor therapy in women 50–79 had small absolute benefits and gains were offset by adverse events (loss of 0.06 discounted QALYs). There were greater gains with extended endocrine therapy for women with node‐positive versus negative cancers, but only women ages 25–49 and 50–59 had a net QALY gain. All gains were reduced with less than 100% treatment completion.

Conclusion

The extension of endocrine therapy from 5 to 10 years modestly improved lifetime breast cancer outcomes, but in some women, treatment‐related adverse events may outweigh benefits.

Effect of a Randomized Trial of a Web-Based Intervention on Patient-Provider Communication About Breast Density

Background: Breast density increases breast cancer risk and decreases mammographic detection. We evaluated a personalized web-based intervention designed to improve breast cancer risk communication between women and their providers. Materials and Methods: This was a secondary outcome analysis of an online randomized trial. Women aged 40-69 years were randomized, February 2017-May 2018, to a control (n = 503) versus intervention website (n = 492). The intervention website included information about breast density, personalized breast cancer risk, chemoprevention, and magnetic resonance imaging. Participants self-reported communication about density with providers (yes/no) at 6 weeks and 12 months. We used logistic regression with generalized estimating equations to evaluate the association of study arm with density communication. In secondary analyses, we tested if the intervention was associated with indicators of patient activation (breast cancer worry, perceived risk, or health care use). Results: Women (mean age 62 years) in the intervention versus control arm were 2.39 times (95% confidence interval [CI] = 1.37-4.18) more likely to report density communication at 6 weeks; this effect persisted at 12 months (odds ratio [OR] = 1.71, 95% CI = 1.25-2.35). At 6 weeks, this effect was only significant among women who reported (OR = 3.23, 95% CI = 1.24-8.40) versus did not report any previous density discussions (OR = 1.64, 95% CI = 0.83-3.26). A quarter of women in each arm never had a density conversation at any time during the study. Conclusions: Despite providing personalized density and risk information, the intervention did not promote density discussions between women and their providers who had not had them previously. This intervention is unlikely to be used clinically to motivate density conversations in women who have not had them before. Clinical trial registration number NCT03029286.

Development and Validation of a Simulation Model-Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions

There is a need for industry-independent decision tools that integrate clinicopathologic features, comorbidities, and genomic information for women with node-negative, invasive, hormone receptor–positive, human epidermal growth factor receptor-2–negative (early-stage) breast cancer.

Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts

Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

Since 2000, our research teams have used computer models of breast cancer to address important clinical and policy-relevant questions as part of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET). Our 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to represent the burden of breast cancer. We have used our models to investigate questions related to breast cancer biology, compare strategies to improve the balance of benefits and harms of screening mammography, and support insights into the delivery of care by modeling outcomes following clinical decisions about breast cancer treatment. Moving forward, our research will continue to use systems modeling to address issues related to reducing the burden of breast cancer including modeling structural inequities affecting racial disparities. Our future work will also leverage lessons from engaging multidisciplinary scientific teams, expand efforts to share modeling resources with other researchers, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

A simulation model-based clinical decision tool to guide personalized treatment based on individual characteristics: Does 21-gene recurrence score assay testing change decisions?

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Background: There is a need for web-based decision tools that integrate clinicopathologic features and genomic information to guide breast cancer therapy for women with node-negative, hormone receptor positive, HER2 negative (“early-stage”) breast cancer. We developed a novel simulation model-based clinical decision tool that provides prognostic estimates of treatment outcomes based on age, tumor size, grade, and comorbidities with and without 21-gene recurrence scores (RS). Methods: We adapted an extant breast cancer simulation model developed within the NCI-funded Cancer Intervention and Surveillance Modeling Network (CISNET) to derive estimates for the 10-year risks of distant recurrence, breast cancer-specific mortality, other cause mortality and life-years gained with endocrine vs. chemo-endocrine therapy for individual women based on their age, tumor size, grade, and comorbidity-level with and without RS test results. The model used an empiric Bayesian analytical approach to combine information from clinical trials, registry and claims data to provide individual estimates. External validation of the model was performed by comparing model-based breast cancer mortality rates and observed rates in the Surveillance Epidemiology and End Results (SEER) registry. Results: Several exemplar profiles were selected to illustrate the clinical utility of the decision tool. For example, the absolute chemotherapy benefit for 10-year distant recurrence risk and life-years gained, without RS testing, and the outcomes if a woman got tested and had a RS 16-20 are provided below for a 40-44-year-old woman and a 65–69-year-old woman diagnosed with a small (≤2cm), intermediate grade tumor and mild comorbidities. Conclusions: Simulation modeling is useful for creating clinical decision tools to support shared decision making for early-stage breast cancer treatment.[Table: see text]

Characterizing patient-oncologist communication in genomic tumor testing: The 21-gene recurrence score as an exemplar

OBJECTIVE

Women with early-stage, ER + breast cancer are recommend to receive genomic profiling tests, such as the 21-gene Recurrence Score (RS) test, to guide treatment decisions. We examined test- and treatment-related information discussed and the associations between RS categories and aspects of communication during patient-oncologist clinical encounters.

METHODS

As part of a larger trial, clinical encounters (N = 46) were audiorecorded and coded for 1) RS- and treatment-related information, 2) shared decision making, 3) patient active participation, and 4) oncologist patient-centered communication. We examined differences by RS category using mixed models, adjusting for nesting within oncologist.

RESULTS

Patients with a high RS were more likely to receive a chemotherapy recommendation (p < .01), hear about the risks/side effects of chemotherapy (p < .01), and offer their preferences (p = .02) than those with intermediate or low RS. Elements of shared decision making increased with RS. Oncologist patient-centered communication (M = 4.09/5, SD = .25) and patient active participation (M = 3.5/4, SD = 1.0) were high across RS.

CONCLUSION

Findings suggest that disease severity, rather than clinical uncertainty, impact treatment recommendations and shared decision making.

PRACTICE IMPLICATIONS

Oncologists adjust test- and treatment-related information and shared decision making by disease severity. This information provides a framework to inform decision making in complex cancer and genomics settings.

Cost-Effectiveness of Smoking Cessation Interventions in the Lung Cancer Screening Setting: A Simulation Study

BACKGROUND

Guidelines recommend offering cessation interventions to smokers eligible for lung cancer screening, but there is little data comparing specific cessation approaches in this setting. We compared the benefits and costs of different smoking cessation interventions to help screening programs select specific cessation approaches.

METHODS

We conducted a societal-perspective cost-effectiveness analysis using a Cancer Intervention and Surveillance Modeling Network model simulating individuals born in 1960 over their lifetimes. Model inputs were derived from Medicare, national cancer registries, published studies, and micro-costing of cessation interventions. We modeled annual lung cancer screening following 2014 US Preventive Services Task Force guidelines plus cessation interventions offered to current smokers at first screen, including pharmacotherapy only or pharmacotherapy with electronic and/or web-based, telephone, individual, or group counseling. Outcomes included lung cancer cases and deaths, life-years saved, quality-adjusted life-years (QALYs) saved, costs, and incremental cost-effectiveness ratios.

RESULTS

Compared with screening alone, all cessation interventions decreased cases of and deaths from lung cancer. Compared incrementally, efficient cessation strategies included pharmacotherapy with either web-based cessation ($555 per QALY), telephone counseling ($7562 per QALY), or individual counseling ($35 531 per QALY). Cessation interventions continued to have costs per QALY well below accepted willingness to pay thresholds even with the lowest intervention effects and was more cost-effective in cohorts with higher smoking prevalence.

CONCLUSION

All smoking cessation interventions delivered with lung cancer screening are likely to provide benefits at reasonable costs. Because the differences between approaches were small, the choice of intervention should be guided by practical concerns such as staff training and availability.

Effect of Personalized Breast Cancer Risk Tool on Chemoprevention and Breast Imaging: ENGAGED-2 Trial

Background

Limited evidence exists about how to communicate breast density-informed breast cancer risk to women at elevated risk to motivate cancer prevention.

Methods

We conducted a randomized controlled trial evaluating a web-based intervention incorporating personalized breast cancer risk, information on chemoprevention, and values clarification on chemoprevention uptake vs active control. Eligible women aged 40-69 years with normal mammograms and elevated 5-year breast cancer risk were recruited from Kaiser Permanente Washington from February 2017 to May 2018. Chemoprevention uptake was measured as any prescription for raloxifene or tamoxifen within 12 months from baseline in electronic health record pharmacy data. Secondary outcomes included breast magnetic resonance imaging (MRI), mammography use, self-reported distress, and communication with providers. We calculated unadjusted odds ratios (ORs) using logistic regression models and mean differences using analysis of covariance models with 95% confidence intervals (CIs) with generalized estimating equations.

Results

We randomly assigned 995 women to the intervention arm (n = 492) or control arm (n = 503). The intervention (vs control) had no effect on chemoprevention uptake (OR = 1.04, 95% CI = 0.07 to 16.62). The intervention increased breast MRI use (OR = 5.65, 95% CI = 1.61 to 19.74) while maintaining annual mammography (OR = 0.98, 95% CI = 0.75 to 1.28). Women in the intervention (vs control) arm had 5.67-times higher odds of having discussed chemoprevention or breast MRI with provider by 6 weeks (OR = 5.67, 95% CI = 2.47 to 13.03) and 2.36-times higher odds by 12 months (OR = 2.36, 95% CI = 1.65 to 3.37). No measurable differences in distress were detected.

Conclusions

A web-based, patient-level intervention activated women at elevated 5-year breast cancer risk to engage in clinical discussions about chemoprevention, but uptake remained low.

Question Prompt List to Support Patient-Provider Communication in the Use of the 21-Gene Recurrence Test: Feasibility, Acceptability, and Outcomes

PURPOSE

The 21-gene recurrence score (RS) assay is used to guide breast cancer treatment decisions but can be poorly understood by patients. We examined the effects of a question prompt list (QPL) on knowledge, distress, and decisional conflict related to genomic testing and treatment in early-stage breast cancer.

METHODS

We describe the feasibility and acceptability of the QPL and the impact of the QPL on knowledge, distress, and decisional conflict before and after the receipt of the QPL (MEND 2, N = 65). We also compared distress and decisional conflict between women who received the QPL (MEND 2, N = 65) and a comparable group of women who did not receive the QPL who participated in an earlier observational study within the same clinics (MEND 1, N = 136).

RESULTS

MEND 2 participants indicated high acceptability and feasibility using the QPL. Knowledge increased post-QPL (P < .01) but did not decrease distress. Decisional conflict was lower among women in MEND 2 compared with those in MEND 1 (P < .01), with no statistically significant differences in distress.

CONCLUSION

The findings suggest that the QPL is feasible, acceptable, can improve knowledge and decrease decisional conflict in the large group of women deciding treatment while integrating RS test results.

An Approach to Identify Delivery of Palliative Radiation Therapy Using Health Care Claims Data: A Proof-of-Concept Application of a Visual Analytics Tool

PURPOSE

There is limited information on the use of data visualization tools for health services research applications. We provide a proof-of-concept application that focuses on claims-based measures of palliative radiation therapy. We investigate whether a guided, data-driven investigation contributes information for subsequent statistical analysis and algorithm development.

METHODS

This retrospective cohort study used linked registry and claims data on men who were diagnosed with stage IV M0 or stage IV M1b prostate cancer between 2005 and 2009, with associated claims from 2005 through 2010, and receiving radiation therapy. Preprocessing of data was accomplished by using EventFlow software to investigate longitudinal patterns in claims for radiation therapy in the 13 months after cancer diagnosis. Guided by results from EventFlow, we developed descriptive statistics to investigate the length of radiation therapy, use of bone metastasis coding, and mortality between M1b and M0 patients.

RESULTS

A total of 1,151 patients met the inclusion criteria. Taking advantage of the novel aggregation capability of EventFlow, we observed differences in the length of radiation therapy and the use of bone metastasis coding between men with (M1b) and without (M0) a diagnosis of bone metastasis. Seventy-nine percent of M1b patients received radiation for a duration ≤ 4 weeks, which suggested palliative radiation (to the bone). Seventy-six percent of M0 patients received radiation for ≥ 6 weeks, which suggested radiation to the prostate. Mortality was higher among those who received a shorter duration of radiation therapy compared with those who received a longer duration of therapy.

CONCLUSION

Use of EventFlow, followed by statistical analysis of the linked registry and claims data, identified useful components of a claims-based measure of radiation to the bone.

Effects of Radiotherapy in Early-Stage, Low-Recurrence Risk, Hormone-Sensitive Breast Cancer

Background

Radiotherapy after breast conservation has become the standard of care. Prior meta-analyses on effects of radiotherapy predated availability of gene expression profiling (GEP) to assess recurrence risk and/or did not include all relevant outcomes. This analysis used GEP information with pooled individual-level data to evaluate the impact of omitting radiotherapy on recurrence and mortality.

Methods

We considered trials that evaluated or administered radiotherapy after lumpectomy in women with low-risk breast cancer. Women included had undergone lumpectomy and were treated with hormonal therapy for stage I, ER+ and/or PR+, HER2- breast cancer with Oncotype scores no greater than 18. Recurrence-free interval (RFI), type of RFI (locoregional or distant), and breast cancer-specific and overall survival were compared between no radiotherapy and radiotherapy using adjusted Cox models. All statistical tests were two-sided.

Results

The final sample included 1778 women from seven trials. Omission of radiotherapy was associated with an overall adjusted hazard ratio of 2.59 (95% confidence interval [CI] = 1.38 to 4.89, P = .003) for RFI. There was a statistically significant increase in any first locoregional recurrence (P = .001), but not distant recurrence events (P = .90), or breast cancer-specific (P = .85) or overall survival (P = .61). Five-year RFI rate was high (93.5% for no radiotherapy vs 97.9% for radiotherapy; absolute reduction = 4.4%, 95% CI = 0.7% to 8.1%, P = .03). The effects of radiotherapy varied across subgroups, with lower RFI rates for those with Oncotype scores of less than 11 (vs 11-18), older (vs younger), and ER+/PR+ status (vs other).

Conclusions

Omission of radiotherapy in hormone-sensitive patients with low recurrence risk may lead to a modest increase in locoregional recurrence event rates, but does not appear to increase the rate of distant recurrence or death.

Simulation Modeling of Cancer Clinical Trials: Application to Omitting Radiotherapy in Low-risk Breast Cancer

Background

We used two models to simulate a proposed noninferiority trial of radiotherapy (RT) omission in low-risk invasive breast cancer to illustrate how modeling could be used to predict the trial's outcomes, inform trial design, and contribute to practice debates.

Methods

The proposed trial was a prospective randomized trial of no-RT vs RT in women age 40 to 74 years undergoing lumpectomy and endocrine therapy for hormone receptor-positive, human epidermal growth factor receptor 2-negative, stage I breast cancer with an Oncotype DX score of 18 or lower. The primary endpoint was recurrence-free interval (RFI), including locoregional recurrence, distant recurrence, and breast cancer death. Noninferiority required the two-sided 90% confidence interval of the RFI hazard ratio (HR) for no-RT vs RT to be entirely below 1.7. Model inputs included published data. The trial was simulated 1000 times, and results were summarized as percent concluding noninferiority and mean (standard deviation) of hazard ratios for Model GE and Model M, respectively.

Results

Noninferiority was demonstrated in 18.0% and 3.7% for the two models. The respective means (SD) of the RFI hazard ratios were 1.8 (0.7) and 2.4 (0.9); most were locoregional recurrences. The mean five-year RFI rates for no-RT vs RT (SD) were 92.7% (2.9%) vs 95.5% (2.2%) and 88.4% (2.0%) vs 94.5% (1.6%). Both models showed little or no difference in breast cancer-specific or overall survival. Alternative definitions of low risk based on combinations of age and grade produced similar results.

Conclusions

The proposed trial was unlikely to show noninferiority of omitting radiotherapy even using alternative definitions of low-risk, as the endpoint included local recurrence. Future trials regarding radiotherapy should address absolute reduction in recurrence and impact of type of recurrence on the patient.

Structure, Function, and Applications of the Georgetown-Einstein (GE) Breast Cancer Simulation Model

BACKGROUND

The Georgetown University-Albert Einstein College of Medicine breast cancer simulation model (Model GE) has evolved over time in structure and function to reflect advances in knowledge about breast cancer, improvements in early detection and treatment technology, and progress in computing resources. This article describes the model and provides examples of model applications.

METHODS

The model is a discrete events microsimulation of single-life histories of women from multiple birth cohorts. Events are simulated in the absence of screening and treatment, and interventions are then applied to assess their impact on population breast cancer trends. The model accommodates differences in natural history associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) biomarkers, as well as conventional breast cancer risk factors. The approach for simulating breast cancer natural history is phenomenological, relying on dates, stage, and age of clinical and screen detection for a tumor molecular subtype without explicitly modeling tumor growth. The inputs to the model are regularly updated to reflect current practice. Numerous technical modifications, including the use of object-oriented programming (C++), and more efficient algorithms, along with hardware advances, have increased program efficiency permitting simulations of large samples.

RESULTS

The model results consistently match key temporal trends in US breast cancer incidence and mortality.

CONCLUSION

The model has been used in collaboration with other CISNET models to assess cancer control policies and will be applied to evaluate clinical trial design, recurrence risk, and polygenic risk-based screening.

Simulation Modeling to Extend Clinical Trials of Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Early Breast Cancer

Purpose

The Trial Assigning Individualized Options for Treatment (TAILORx) found chemotherapy could be omitted in many women with hormone receptor-positive, HER2-negative, node-negative breast cancer and 21-gene recurrence scores (RS) 11-25, but left unanswered questions. We used simulation modeling to fill these gaps.

Methods

We simulated women eligible for TAILORx using joint distributions of patient and tumor characteristics and RS from TAILORx data; treatment effects by RS from other trials; and competing mortality from the Surveillance, Epidemiology, and End Results program database. The model simulations replicated TAILORx design, and then tested treatment effects on 9-year distant recurrence-free survival (DRFS) in 14 new scenarios: eight subgroups defined by age (≤50 and >50 years) and 21-gene RS (11-25/16-25/16-20/21-25); six different RS cut points among women ages 18-75 years (16-25, 16-20, 21-25, 26-30, 26-100); and 20-year follow-up. Mean hazard ratios SD, and DRFS rates are reported from 1000 simulations.

Results

The simulation results closely replicated TAILORx findings, with 75% of simulated trials showing noninferiority for chemotherapy omission. There was a mean DRFS hazard ratio of 1.79 (0.94) for endocrine vs chemoendocrine therapy among women ages 50 years and younger with RS 16-25; the DFRS rates were 91.6% (0.04) for endocrine and 94.8% (0.01) for chemoendocrine therapy. When treatment was randomly assigned among women ages 18-75 years with RS 26-30, the mean DRFS hazard ratio for endocrine vs chemoendocrine therapy was 1.60 (0.83). The conclusions were unchanged at 20-year follow-up.

Conclusions

Our results confirmed a small benefit in chemotherapy among women aged 50 years and younger with RS 16-25. Simulation modeling is useful to extend clinical trials, indicate how uncertainty might affect results, and power decision tools to support broader practice discussions.

Epidemiological Determinants of Advanced Prostate Cancer in Elderly Men in the United States

In this study, we examined the effects of individual-level and area-level characteristics on advanced prostate cancer diagnosis among Medicare eligible older men (ages 70+ years). We analyzed patients from the linked Surveillance, Epidemiology, and End Results (SEER)-Medicare database (2000-2007) linked to US Census and County Business Patterns data. Cluster-adjusted logistic regression models were used to quantify the effects of individual preventive health behavior, clinical and demographic characteristics, area-level health services supply, and socioeconomic characteristics on stage at diagnosis. The fully adjusted model was used to estimate county-specific effects and predicted probabilities of advanced prostate cancer. In the adjusted analyses, low intensity of annual prostate-specific antigen (PSA) testing and other preventive health behavior, high comorbidity, African American race, and lower county socioeconomic and health services supply characteristics were statistically significantly associated with a higher likelihood of distant prostate cancer diagnosis. The fully adjusted predicted proportions of advanced prostate cancer diagnosis across 158 counties ranged from 3% to 15% (mean: 6%, SD: 7%). County-level socioeconomic and health services supply characteristics, individual-level preventive health behavior, demographic and clinical characteristics are determinants of advanced stage prostate cancer diagnosis among older Medicare beneficiaries; other health care-related factors such as family history, lifestyle choices, and health-seeking behavior should also be considered as explanatory factors.

An ecological approach to monitor geographic disparities in cancer outcomes

Background

Area-level indices are widely used to assess the impact of socio-environmental characteristics on cancer outcomes. While area-level measures of socioeconomic status (SES) have been previously used in cancer settings, fewer studies have focused on evaluating the impact of area-level health services supply (HSS) characteristics on cancer outcomes. Moreover, there is significant variation in the methods and constructs used to create area-level indices.

Methods

In this study, we introduced a psychometrically-induced, reproducible approach to develop area-level HSS and SES indices. We assessed the utility of these indices in detecting the effects of area-level characteristics on prostate, breast, and lung cancer incidence and stage at diagnosis in the US. The information on county-level SES and HSS characteristics were extracted from US Census, County Business Patterns data and Area Health Resource Files. The Surveillance, Epidemiology, and End Results database was used to identify individuals diagnosed with cancer from 2010 to 2012. SES and HSS indices were developed and linked to 3-year age-adjusted cancer incidence rates. SES and HSS indices empirically summarized the level of employment, education, poverty and income, and the availability of health care facilities and health professionals within counties.

Results

SES and HSS models demonstrated good fit (TLI = 0.98 and 0.96, respectively) and internal consistency (alpha = 0.85 and 0.95, respectively). Increasing SES and HSS were associated with increasing prostate and breast cancer and decreasing lung cancer incidence rates. The results varied by stage at diagnosis and race.

Conclusion

Composite county-level measures of SES and HSS were effective in ranking counties and detecting gradients in cancer incidence and stage at diagnosis. Thus, these measures provide valuable tools for monitoring geographic disparities in cancer outcomes.

Using tumor genomic profile testing and comorbidity level to personalize chemotherapy decisions among older patients with early-stage breast cancer

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Background: Under-representation of women ages 65+ (“older”) in the trials may limit clinical translation of results to this growing population. We used simulation modeling to estimate chemotherapy outcomes by age and comorbidity level among older women with early stage, estrogen-receptor+/HER2- breast cancers with an Oncotype DX score of 26+. Methods: A discrete-time stochastic state-transition model synthesized data from population studies and clinical trials to estimate outcomes over a 25-year horizon for subgroups of women based on age (65-69, 70-74, 75-79, and 80-89) and comorbidity levels (no/low, moderate, and severe). Age-, comorbidity-specific non-cancer survival was derived from a random 5% sample of women enrolled in the Medicare Part A and B program from 1992 to 2005 and included in the SEER areas. Outcomes included life years (LYs), quality-adjusted life years (QALYs), and breast cancer and other-cause mortality with chemoendocrine therapy or endocrine therapy alone. Sensitivity analysis tested the impact on outcomes of varying values of uncertain parameters. Results: Women with life expectancies of ≥ 7 years had net gains of 0.17 to 0.45 LYs (2.0-5.4 months) with chemotherapy; this group was mainly women aged 65-69 and 70-74 with no/low or moderate comorbidity. Women destined to develop distant recurrence gained between 4.2-10.4 months. LYs were reduced by chemotherapy toxicity. The majority of women died of other causes, ranging from 59% to 98% across all age groups and comorbidity levels, but chemotherapy reduced breast cancer mortality by 14.5% and 25.7% among women ages 65-69 and 70-74 with no/low comorbidity, respectively. Results were robust in sensitivity analyses, and chemotherapy improved all outcomes as treatment efficacy increased, assuming no change in toxicity. Conclusions: Older women with early stage, estrogen-receptor+/HER2- breast cancers with Oncotype DX scores of 26+ may benefit from chemotherapy, when both conditions of age <75 and comorbidity at no/low or moderate level can be met. Personalized treatment decisions for older women will ultimately depend on comorbidity-specific lifespan and individual preferences for the balance of benefits and harms of chemotherapy.

Simulation modeling of the effects of adjuvant chemotherapy in early-stage breast cancer

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Background: The recent Trial Assigning Individualized Options for Treatment (TAILORx) was practice changing. However, several important questions remain unanswered about therapy for women with early stage, hormone receptor positive (HR+), HER2- breast cancers, including chemotherapy effects by age and different Oncotype recurrence score (RS) cut-points, and longer follow-up. We developed a simulation model to extend the trial results to begin to fill these gaps. Methods: We developed a simulation model using an empirical Bayesian approach to simulate women eligible for the TAILORx trial. The joint distributions of patient and tumor characteristics were derived from de-identified TAILORx data. The remaining inputs used data independent of the trial, including SEER, the Oxford Overview, and other trials. TAILORx was simulated to examine the effects of chemotherapy (+ hormonal Rx) vs. hormonal Rx alone on distant recurrence-free survival (DRFS) at 9- and 20-years by age (≤50 and >50 years) and RS (11-25 and 16-25). We also evaluated the effects of chemotherapy in women with RS 26-30. Hazard ratios (HR) were determined using Cox regressions and DRFS by treatment were derived from Kaplan-Meier curves. We report the mean results from 1000 trial simulations, where each simulation randomly sampled values for each parameter from their observed joint distribution. Finally, the original trial was replicated for model validation. Results: The model closely replicated actual trial results. Sample sizes ranged from 7000-10000. The model estimated that chemotherapy improved DFRS in women aged ≤50 with RS 16-25 (Table). The 20-year event rates remained low in the 11-25 category. Among women with RS 26-30, HR for no chemo vs. chemo was 1.38 (95% CI:1.18-1.58). Conclusions: Simulation suggests that chemotherapy may reduce distant recurrence in younger women at different cut points between 11-25. Simulation modeling may be useful to translate trial results to broader population subgroups than those possible to test in RCTs. Nine-year distant recurrence-free survival by chemotherapy. [Table: see text]

Evidence-based sizing of non-inferiority trials using decision models

Background

There are significant challenges to the successful conduct of non-inferiority trials because they require large numbers to demonstrate that an alternative intervention is “not too much worse” than the standard. In this paper, we present a novel strategy for designing non-inferiority trials using an approach for determining the appropriate non-inferiority margin (δ), which explicitly balances the benefits of interventions in the two arms of the study (e.g. lower recurrence rate or better survival) with the burden of interventions (e.g. toxicity, pain), and early and late-term morbidity.

Methods

We use a decision analytic approach to simulate a trial using a fixed value for the trial outcome of interest (e.g. cancer incidence or recurrence) under the standard intervention (p_S) and systematically varying the incidence of the outcome in the alternative intervention (p_A). The non-inferiority margin, p_A – p_S = δ, is reached when the lower event rate of the standard therapy counterbalances the higher event rate but improved morbidity burden of the alternative. We consider the appropriate non-inferiority margin as the tipping point at which the quality-adjusted life-years saved in the two arms are equal.

Results

Using the European Polyp Surveillance non-inferiority trial as an example, our decision analytic approach suggests an appropriate non-inferiority margin, defined here as the difference between the two study arms in the 10-year risk of being diagnosed with colorectal cancer, of 0.42% rather than the 0.50% used to design the trial. The size of the non-inferiority margin was smaller for higher assumed burden of colonoscopies.

Conclusions

The example demonstrates that applying our proposed method appears feasible in real-world settings and offers the benefits of more explicit and rigorous quantification of the various considerations relevant for determining a non-inferiority margin and associated trial sample size.

The impact of gene expression profile testing on confidence in chemotherapy decisions and prognostic expectations

PURPOSE

Little is known about whether gene expression profile (GEP) testing and specific recurrence scores (e.g., medium risk) improve women's confidence in their chemotherapy decision or perceived recurrence risk. We evaluate the relationship between these outcomes and GEP testing.

METHODS

We surveyed women eligible for GEP testing (stage I or II, Gr1-2, ER+, HER2-) identified through the Surveillance, Epidemiology, and End Results (SEER) Registry of Washington or Kaiser Permanente Northern California from 2012 to 2016, approximately 0-4 years from diagnosis (N = 904, RR = 45.4%). Confidence in chemotherapy was measured as confident (Very, completely) versus Not Confident (Somewhat, A little, Not At All); perceived risk recurrence was recorded numerically (0-100%). Women reported their GEP test receipt (Yes, No, Unknown) and risk recurrence score (High, Intermediate, Low, Unknown). In our analytic sample (N = 833), we propensity score weighted the three test receipt cohorts and used propensity weighted multivariable regressions to examine associations between the outcomes and the three test receipt cohorts, with receipt stratified by score.

RESULTS

29.5% reported an unknown GEP test receipt; 86% being confident. Compared to no test receipt, an intermediate score (aOR 0.34; 95% CI 0.20-0.58), unknown score (aOR 0.09; 95% CI 0.05-0.18), and unknown test receipt (aOR 0.37; 95% CI 0.24-0.57) were less likely to report confidence. Most women greatly overestimated their recurrence risk regardless of their test receipt or score.

CONCLUSIONS

GEP testing was not associated with greater confidence in chemotherapy decisions. Better communication about GEP testing and the implications for recurrence risk may improve women's decisional confidence.

Multilevel Influences on Patient-Oncologist Communication about Genomic Test Results: Oncologist Perspectives

Thousands of women with early-stage breast cancer receive gene-expression profile (GEP) tests to guide chemotherapy decisions. However, many patients report a poor understanding of how their test results inform treatment decision-making. We applied models of patient-centered communication and informed decision-making to assess which variables oncologists' perceive as most influential to effective communication with their patients about GEP results and intervention modalities and approaches that could support more effective conversations about treatment decisions in routine clinical care. Medical oncologists who were part of a practice group in the mid-Atlantic US completed an online, cross-sectional survey in 2016. These data were merged with de-identified electronic patient and practice data. Of the 83 oncologists contacted, 29 completed the survey (35% response rate, representing 52% of the test-eligible patients in the practice network). There were no significant differences between survey responders and nonresponders. Oncologists reported patient-related variables as most influential, including performance status (65.5%), pretesting preferences for chemotherapy (55.2%), and comprehension of complex test results (55.2%). Oncologists endorsed their experience with testing (58.6%) and their own confidence in using the test results (48.3%) as influential as well. They indicated that a clinical decision support tool incorporating patient comorbidities, age, and potential benefits from chemotherapy would support their own practice and that they could share these results and other means of communication support using print materials (79.3%) with their patients in clinic (72.4%). These preferred intervention characteristics could be integrated into routine care, ultimately facilitating more effective communication about genomic testing (such as GEP) and its role in treatment selection.

Cost Effectiveness of Gene Expression Profile Testing in Community Practice

Purpose Gene expression profile (GEP) testing can support chemotherapy decision making for patients with early-stage, estrogen receptor-positive, human epidermal growth factor 2-negative breast cancers. This study evaluated the cost effectiveness of one GEP test, Onco type DX (Genomic Health, Redwood City, CA), in community practice with test-eligible patients age 40 to 79 years. Methods A simulation model compared 25-year societal incremental costs and quality-adjusted life-years (QALYs) of community Onco type DX use from 2005 to 2012 versus usual care in the pretesting era (2000 to 2004). Inputs included Onco type DX and chemotherapy data from an integrated health care system and national and published data on Onco type DX accuracy, chemotherapy effectiveness, utilities, survival and recurrence, and Medicare and patient costs. Sensitivity analyses varied individual parameters; results were also estimated for ideal conditions (ie, 100% testing and adherence to test-suggested treatment, perfect test accuracy, considering test effects on reassurance or worry, and lowest costs). Results Twenty-four percent of test-eligible patients had Onco type DX testing. Testing was higher in younger patients and patients with stage I disease ( v stage IIA), and 75.3% and 10.2% of patients with high and low recurrence risk scores received chemotherapy, respectively. The cost-effectiveness ratio for testing ( v usual care) was $188,125 per QALY. Considering test effects on worry versus reassurance decreased the cost-effectiveness ratio to $58,431 per QALY. With perfect test accuracy, the cost-effectiveness ratio was $28,947 per QALY, and under ideal conditions, it was $39,496 per QALY. Conclusion GEP testing is likely to have a high cost-effectiveness ratio on the basis of community practice patterns. However, realistic variations in assumptions about key variables could result in GEP testing having cost-effectiveness ratios in the range of other accepted interventions. The differences in cost-effectiveness ratios on the basis of community versus ideal conditions underscore the importance of considering real-world implementation when assessing the new technology.

Cost Prediction Using a Survival Grouping Algorithm: An Application to Incident Prostate Cancer Cases

BACKGROUND

Prognostic classification approaches are commonly used in clinical practice to predict health outcomes. However, there has been limited focus on use of the general approach for predicting costs. We applied a grouping algorithm designed for large-scale data sets and multiple prognostic factors to investigate whether it improves cost prediction among older Medicare beneficiaries diagnosed with prostate cancer.

METHODS

We analysed the linked Surveillance, Epidemiology and End Results (SEER)-Medicare data, which included data from 2000 through 2009 for men diagnosed with incident prostate cancer between 2000 and 2007. We split the survival data into two data sets (D0 and D1) of equal size. We trained the classifier of the Grouping Algorithm for Cancer Data (GACD) on D0 and tested it on D1. The prognostic factors included cancer stage, age, race and performance status proxies. We calculated the average difference between observed D1 costs and predicted D1 costs at 5 years post-diagnosis with and without the GACD.

RESULTS

The sample included 110,843 men with prostate cancer. The median age of the sample was 74 years, and 10% were African American. The average difference (mean absolute error [MAE]) per person between the real and predicted total 5-year cost was US$41,525 (MAE US$41,790; 95% confidence interval [CI] US$41,421-42,158) with the GACD and US$43,113 (MAE US$43,639; 95% CI US$43,062-44,217) without the GACD. The 5-year cost prediction without grouping resulted in a sample overestimate of US$79,544,508.

CONCLUSION

The grouping algorithm developed for complex, large-scale data improves the prediction of 5-year costs. The prediction accuracy could be improved by utilization of a richer set of prognostic factors and refinement of categorical specifications.

ReCAP: Impact of Multidisciplinary Care on Processes of Cancer Care: A Multi-Institutional Study

PURPOSE

The role of multidisciplinary care (MDC) on cancer care processes is not fully understood. We investigated the impact of MDC on the processes of care at cancer centers within the National Cancer Institute Community Cancer Centers Program (NCCCP).

METHODS

The study used data from patients diagnosed with stage IIB to III rectal cancer, stage III colon cancer, and stage III non–small-cell lung cancer at 14 NCCCP cancer centers from 2007 to 2012. We used an MDC development assessment tool—with levels ranging from evolving MDC (low) to achieving excellence (high)—to measure the level of MDC implementation in seven MDC areas, such as case planning and physician engagement. Descriptive statistics and cluster-adjusted regression models quantified the association between MDC implementation and processes of care, including time from diagnosis to treatment receipt.

RESULTS

A total of 1,079 patients were examined. Compared with patients with colon cancer treated at cancer centers reporting low MDC scores, time to treatment receipt was shorter for patients with colon cancer treated at cancer centers reporting high or moderate MDC scores for physician engagement (hazard ratio [HR] for high physician engagement, 2.66; 95% CI, 1.70 to 4.17; HR for moderate physician engagement, 1.50; 95% CI, 1.19 to 1.89) and longer for patients with colon cancer treated at cancer centers reporting high 2MDC scores for case planning (HR, 0.65; 95% CI, 0.49 to 0.85). Results for patients with rectal cancer were qualitatively similar, and there was no statistically significant difference among patients with lung cancer.

CONCLUSION

MDC implementation level was associated with processes of care, and direction of association varied across MDC assessment areas.

Racial variation in the clinical and economic burden of skeletal-related events among elderly men with stage IV metastatic prostate cancer

Prostate cancer (PCa) outcomes vary widely among African American (AA) and non-Hispanic White (NHW) men. The authors investigated racial variation in the incidence of skeletal-related events (SREs) and SRE-related healthcare costs among AA and NHW men, a topic that has received limited attention in the literature. AA and NHW men diagnosed with metastatic PCa were identified from the linked Surveillance, Epidemiology and End Results-Medicare dataset. The sample included 6455 men with metastatic PCa, including 5420 NHW men and 1035 AA men. Approximately 16% experienced SREs during follow-up. AA men were less likely to experience SREs compared with NHW men, controlling for individual characteristics (adjusted odds ratio: 0.79; 95% CI: 0.66- 0.94). The SRE-specific costs were US$35,725 (US$22,190-US$49,260) among AA men and US$25,896 (US$21,669-US$30,123) among NHW men. Although AA men were less likely to experience SREs, there were substantial costs attributable to the treatment of SREs among AA men.

Abstract B80: Racial differences in prostate cancer screening practices in U.S. ambulatory care settings prior to and following U.S. Preventive Services Task Force recommendations in 2008

Introduction: In May 2012, the United States Preventive Services Task Force (USPSTF) finalized its recommendation (rec.) against prostate-specific antigen (PSA) screening in all men in the general US population. The implications of the recommendations for PSA screening in African American (AA), Hispanic (H) and White (W) men will not be known for some time. However, the USPSTF's August 2008 rec. against PSA-based screening for men older than 75 years may inform expectations. The objective of this study was to investigate the impact of the 2008 USPSTF rec. on PSA screening practices during routine preventive health exams (PHE) among AA, H, and W men in ambulatory care settings.

Methods: We analyzed a nationally representative sample of visits to office-based physicians' practices from 2005 to 2010 using the National Ambulatory Medical Care Survey (NAMCS). The sample consisted of outpatient visits for PHEs of men over 40 years, without prostate cancer. PHEs were identified using, 1) patient's stated reason and physician-reported major reason for visit indicating a general medical exam or prevention, or 2) physician's recorded diagnosis for general medical exam. The study period was divided into pre-rec. (2005-07) and post-rec. (2008-10) periods. Information including PSA screening during the PHE, patient demographics, physician specialty and type of office setting was collected. Descriptive and bivariate analyses accounting for the NAMCS survey design provided the unadjusted relationships between PSA screening, race/ethnicity, period and age. Generalized estimating equations (GEE) were used to investigate the demographic patterns in the receipt of PSA screening during PHEs occurring around the time of the USPSTF rec.

Results: Application of the inclusion criteria resulted in 6,099 office visits for PHEs from 2005-10. Twenty percent (N=1,241) of the sample was aged ≤75 years, 76% (N=4,630) was W, 10% (N=591) AA and 9% (N=564) H. Overall, 16% received PSAs during a PHE in the pre-rec. period and 18% in the post-rec. period (p=0.14). The percentage of PSA screens received during a PHE in pre-rec. vs. post-rec. period was 13% vs. 16% (p=0.44) among AAs, 9% vs. 14% (p=0.13) among Hs, and 16% vs. 19% (p=0.12) among Ws. The percentage of W men, aged 40 to 75, receiving a PSA test during a PHE (20%) was statistically significantly higher than AAs (16%) and Hs (13%) (p= 0.03). Among those ≥75 years, there was no statistically significant difference in the receipt of PSA screening between AAs (11%), Hs (4%), and Ws (11%) (p=0.07). GEE analysis did not show a statistically significant difference in the covariate-adjusted odds of receiving a PSA screen during a PHE in the post-period compared to the pre-period (p=0.40) or among AAs compared to Ws (p=0.22). Odds of receiving a PSA screen were lower among patients aged ≥75 years compared to patients less than 75 years old (odds ratio=0.51, 95% CI=0.36-0.73; p&lt;0.01). Odds of receiving a PSA screen also were lower among Hs compared to Ws (OR=0.64, 95% CI=0.43-0.96; p=0.03). Odds of receiving a PSA screen were higher for visits to a urologist compared to visits to a general practitioner (OR=4.30, 95% CI=2.92-6.34; p&lt;0.01).

Conclusion: The probability of receiving a PSA screening during a PHE was unchanged during the period immediately prior to and following the 2008 USPSTF rec. against PSA screening for men 75 years and older. At the same time, men 75 years and older (compared to &lt;75 years) and H men (compared to W) were less likely to receive a PSA screening during a PHE. Analyses exploring the impact of the 2012 USPSTF recommendations against PSA screening for all men should consider the impact among race and age subgroups with lower baseline chances of receiving a PSA screen in ambulatory care settings.

Citation Format: Jinani Jayasekera, Eberechukwu Onukwugha. Racial differences in prostate cancer screening practices in U.S. ambulatory care settings prior to and following U.S. Preventive Services Task Force recommendations in 2008. [abstract]. In: Proceedings of the Sixth AACR Conference: The Science of Cancer Health Disparities; Dec 6–9, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2014;23(11 Suppl):Abstract nr B80. doi:10.1158/1538-7755.DISP13-B80

An algorithm to identify delivery of palliative radiation therapy using health care claims data: A proof of concept application of data visualization tools in the prostate cancer (PCa) setting

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Background: Studies using healthcare claims data to investigate the burden of skeletal-related events employ various approaches to identify radiation to the bone (RttB) because billing codes available in claims data do not distinguish RttB from radiation to the prostate gland. We investigated the use of Eventflow data visualization software to identify components of a claims-based algorithm for RttB. Methods: We analyzed data for PCa cases identified in the Surveillance, Epidemiology, and End Results (SEER) registry data linked with Medicare claims. We identified two cohorts of individuals diagnosed between 2005 and 2009 and receiving radiation therapy, C1: diagnosed with incident bone metastasis (BM) according to SEER data; C2: diagnosed with incident stage IV M0 PCa. We defined radiation episodes of care from claims for external beam radiation therapy, radiopharmaceutical therapy, intensity modulated radiotherapy and stereotactic radiosurgery. Eventflow was used to compare cohorts and identify criteria for identifying RttB using claims data. Results: Application of inclusion criteria resulted in 1,491 individuals: 999 in C1 and 492 in C2. Median follow up was 596 days in C1 and 882 days in C2. Average age was 77 years in C1 and 73 years in C2. The median time to radiation therapy was 133 days in C1 compared to 171 days in C2. When requiring a BM diagnosis code on the radiation claim, the median time was 160 days in C1 compared to 514 days in C2. The median time to a fracture was 107 days in C1 compared to 369 days in C2. The median time to bone surgery was 183 days in C1 compared to 447 days in C2. The median time to spinal cord compression was 154 days in C1 compared to 375 days in C2. A BM diagnosis code concurrent with the radiation episode and radiation episodes less than or equal to 4 weeks in length were more common among C1 compared to C2. Conclusions: Analysis of data visualization output indicates that incorporating a bone metastasis code on claims concurrent with a radiation episode of care or the information regarding the length of the radiation episode will be useful for identifying receipt of palliative radiation using claims data.

Racial disparities in urologist visits among elderly men with prostate cancer: a cohort analysis of patient-related and county of residence-related factors

BACKGROUND

Factors contributing to the lower likelihood of urologist follow-up among African American (AA) men diagnosed with prostate cancer may not be strictly related to patient factors. The authors investigated the relationship between crime, poverty, and poor housing, among others, and postdiagnosis urologist visits among AA and white men.

METHODS

The authors used linked cancer registry and Medicare claims data from 1999 through 2007 for men diagnosed with American Joint Committee on Cancer stage I to III prostate cancer. The USA Counties and County Business Patterns data sets provided county-level data. Variance components models reported the percentage of variation attributed to county of residence. Postdiagnosis urologist visits for AA and white men were investigated using logistic and modified Poisson regression models.

RESULTS

A total of 65,635 patients were identified; 87% of whom were non-Hispanic white and 9.3% of whom were non-Hispanic AA. Approximately 16% of men diagnosed with stage I to III prostate cancer did not visit a urologist within 1 year after diagnosis (22% of AA men and 15% of white men). County of residence accounted for 10% of the variation in the visit outcome (13% for AA men and 10% for white men). AA men were more likely to live in counties ranked highest in terms of poverty, occupied housing units with no telephone, and crime. AA men were less likely to see a urologist (odds ratio, 0.65 [95% confidence interval, 0.6-0.71]; rate ratio, 0.94 [95% confidence interval, 0.92-0.95]). The sign and magnitude of the coefficients for the county-level measures differed across race-specific regression models of urologist visits.

CONCLUSIONS

Among older men diagnosed with stage I to III prostate cancer, the social environment appears to contribute to some of the disparities in postdiagnosis urologist visits between AA and white men.

The economic burden of skeletal-related events among elderly men with metastatic prostate cancer

BACKGROUND AND OBJECTIVE

Advanced prostate cancer patients with bone metastasis are predisposed to skeletal complications termed skeletal-related events (SREs). There is limited information available on Medicare costs associated with treating SREs. The objective of this study was to ascertain SRE-related costs among older men with metastatic prostate cancer in the US.

METHODS

We analysed patients aged 66 years or older who were diagnosed with incident stage IV (M1) prostate cancer between 2000 and 2007 from the linked Surveillance, Epidemiology and End Results (SEER)-Medicare dataset. A propensity score for the incidence of an SRE was estimated using a logistic regression model including demographic and clinical baseline variables. Patients with SREs (cases) were matched to patients without SREs (controls) based on the propensity score, length of follow-up (i.e. date of prostate cancer diagnosis to last date of observation) and death. Health resource utilization cost differences between cases and controls over time were compared using generalized linear models. Healthcare costs were examined by type of SRE (pathological fracture only, pathological fracture with concurrent surgery, spinal cord compression only, spinal cord compression with concurrent surgery, and bone surgery only) and by source of care (inpatient, physician/non-institutional provider, skilled nursing facility, outpatient and hospice). All costs were adjusted to 2009 US dollars, using the medical care component of the Consumer Price Index.

RESULTS

Application of the inclusion criteria resulted in 1,131 metastatic prostate cancer patients with SREs and 6,067 patients without SREs during follow-up. The average age of the sample was 79 years, and 14 % were African American. A total of 928 patients with SREs were matched to 928 patients without SREs. The average health care utilization cost of patients with SREs was US$29,696 (95 % confidence interval [CI] US$24,730-US$34,662) higher than that of the controls. The most expensive SRE group was spinal cord compression with concurrent surgery (US$82,868: 95 % CI US$67,472-US$98,264) followed by bone surgery only (US$37,496: 95 % CI US$29,684-US$45,308), pathological fracture with concurrent surgery (US$34,169: 95 % CI US$25,837-US$ 42,501), spinal cord compression only (US$25,793: 95 % CI US$20,933-US$30,653) and pathological fracture only (US$14,649: 95 % CI US$6,537-US$22,761). The largest cost difference by source of care was observed for hospitalizations (p < 0.01).

CONCLUSION

Metastatic prostate cancer patients with SREs incur higher costs compared to similar patients without SREs. SRE costs among older stage IV (M1) prostate cancer patients vary by SRE type, with spinal cord compression and concurrent surgery costing at least twice as much as other SREs.

Incremental cost (IC) analysis of skeletal related events (SREs) among elderly men with stage IV metastatic (M1) prostate cancer (PCa)

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Background: Patients diagnosed with stage IV M1PCa are predisposed to SREs, such as pathologic fracture (PF), spinal cord compression (SCC) and bone surgery (BS). There is limited information in the literature regarding the ICs associated with SREs among stage IV M1 PCa patients. Methods: We analyzed patients aged 66+ yrs diagnosed with incident stage IV M1 PCa between 2000 and 2007 from the linked SEER-Medicare dataset. Five mutually exclusive SRE categories were created: PF only, PF+concurrent surgery (SRG), SCC only, SCC+SRG, and BS only. Patients with multiple SREs were excluded. A propensity score for the incidence of an SRE was estimated using a logistic regression model including baseline demographic and clinical variables, diagnosis year and SEER region. Patients with SREs (cases) were matched to those without SREs (controls), based on the propensity score. The date of SRE of a case was assigned to a matched control, and the 12 month pre- and post-SRE costs were calculated. A difference-in-difference method was used to estimate the ICs (post-pre) for cases vs. controls. The analysis was conducted from a US Medicare perspective. Results: Application of inclusion criteria resulted in 1,131 stage IV M1 PCa patients with SREs. The average age was 78 yrs and 12% were African American. Using the propensity score, 1,031 cases were matched with 1,031 controls and allocated to the following SRE groups: PF+SRG (n=134), PF only (n=143), SCC+SRG (n=40), SCC only (n=538) and BS only (n=176). The average IC per SRE was $30,548 (vs. controls). The most expensive SRE group was SCC+SRG with a total average IC of $62,412, followed by BS only ($37,554), PF+SRG ($35,520), SCC ($28,027), and PF only ($17,839). Inpatient costs were the major driver of ICs followed by physician/non-institutional provider and skilled nursing facility (SNF) costs. Conclusions: The ICs associated with an SRE are significant. The IC of an SRE varies by type of SRE and service category. [Table: see text]

Characteristics of stage III and IV M0 prostate cancer (PCa) patients in SEER-Medicare who develop bone metastasis (BM) following diagnosis

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Background: BM is diagnosed in 70-80% of men with metastatic PCa. Less is known about the timing of BM diagnosis following incident non-metastatic PCa and associated patient characteristics. In this study, we determined the incidence and timing of post-diagnosis BM (BMpd) among PCa patients (pts) by incident stage, age, race and year of diagnosis using a large observational dataset. Methods: We analyzed pts aged 66 or older from the linked Surveillance, Epidemiology, and End Results and Medicare (SEER-Medicare) database. Pts with PCa were identified between 2000 and 2007 and were followed until death, Medicare disenrollment, HMO enrollment, or end of the study (December 31, 2009). The cohort included incident stage III and IV(M0) PCa in SEER, and identified BM occurring either within (i.e., +/-) 1 month of the SEER diagnosis month (BM90) or beyond the initial 90-day window (BMgt90) based on the presence of at least one inpatient or one outpatient claim with a diagnosis code of 198.5. We calculated summary and chi-square statistics to examine BMpd, BM90, and BMgt90 by incident stage, age, race and year of PCa diagnosis. Results: Among 9,188 Stage III (72%) and IV(M0) (28%) PCa pts who met inclusion/exclusion criteria, 14.6% (n=1,345) had BMpd: 2.3% (n= 217) had BM90 and 12.3% (n=1,128) had BMgt90. Average age was 72 years and 9% were African American (AA). Incidence of BMpd varied by stage (stage III: 11%; stage IV/M0: 25%; p<0.001) and by age group (66-74 years: 13%; 75-84 years: 19%; >85 years: 22%; p<0.001) but not by race (White: 15%; AA: 16%; Other: 13%; p=0.49). The diagnosis BM90 and BMgt90 varied with stage (stage III: 2% and 9%; stage IV(M0): 4% and 21%; p<0.0001) and age (66-74 years: 2% and 11%; 75-84 years: 3% and 16%; >85 years: 5% and 17%; p<0.001). The incidence of BM decreased over time whether considering BMpd (19% in 2000 to 9% in 2007; p<0.001), BM90 (4% in 2000 to 2% in 2007; p=0.03) or BMgt90 (16% in 2000 to 6% in 2007; p<0.001). Conclusions: BM occurred in only 2% of incident stage III/IV(M0) PCa pts within 1 month of diagnosis, but nearly 15% were diagnosed with BM during a median follow-up of 57 months. Prevalence of BM was highest in stage IV(M0) and older pts.